Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits

Hugh Churchill

Abstract:

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading
2 strategy to utilize near-term quantum advantages in multiple problems, including
3 machine learning and combinatorial optimization. When applied to specific tasks,
4 the parameters in the quantum circuits are trained to minimize the target function.
5 Although there have been comprehensive studies to improve the performance of
6 the PQCs on practical tasks, the errors caused by the quantum noise downgrade
7 the performance when running on real quantum computers. In particular, when the
8 quantum state is transformed through multiple quantum circuit layers, the effect
9 of the quantum noise happens cumulatively and becomes closer to the maximally
10 mixed state or complete noise. This paper studies the relationship between the
11 quantum noise and the diffusion model. Then, we propose a novel diffusion-
12 inspired learning approach to mitigate the quantum noise in the PQCs and reduce
13 the error for specific tasks. Through our experiments, we illustrate the efficiency
14 of the learning strategy and achieve state-of-the-art performance on classification
15 tasks in the quantum noise scenarios.